Basic Parallel Coordinates Plot — Matplotlib

A parallel coordinates plot visualizes multivariate data by representing each variable as a vertical axis and each observation as a line connecting values across all axes. This technique is powerful for identifying patterns, clusters, and outliers in high-dimensional datasets where traditional 2D plots fall short. It enables simultaneous comparison of multiple variables for each data point.

Basic Parallel Coordinates Plot rendered with Matplotlib

Python source (Matplotlib)

""" anyplot.ai
parallel-basic: Basic Parallel Coordinates Plot
Library: matplotlib 3.11.0 | Python 3.13.14
Quality: 88/100 | Updated: 2026-07-24
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.collections import LineCollection


# Theme tokens (see prompts/default-style-guide.md "Background" + "Theme-adaptive Chrome")
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"

# Imprint palette — 8 hues, theme-independent, hybrid-v3 sort. First 3 positions
# used in canonical order for the 3 species (no semantic color cue applies here).
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3"]

# Data - Iris dataset for multivariate demonstration (embedded for reproducibility)
np.random.seed(42)

# Create Iris-like dataset with realistic measurements
data = {
    "sepal_length": np.concatenate(
        [
            np.random.normal(5.0, 0.35, 50),  # Setosa
            np.random.normal(5.9, 0.52, 50),  # Versicolor
            np.random.normal(6.6, 0.64, 50),  # Virginica
        ]
    ),
    "sepal_width": np.concatenate(
        [
            np.random.normal(3.4, 0.38, 50),  # Setosa
            np.random.normal(2.8, 0.31, 50),  # Versicolor
            np.random.normal(3.0, 0.32, 50),  # Virginica
        ]
    ),
    "petal_length": np.concatenate(
        [
            np.random.normal(1.5, 0.17, 50),  # Setosa
            np.random.normal(4.3, 0.47, 50),  # Versicolor
            np.random.normal(5.5, 0.55, 50),  # Virginica
        ]
    ),
    "petal_width": np.concatenate(
        [
            np.random.normal(0.2, 0.11, 50),  # Setosa
            np.random.normal(1.3, 0.20, 50),  # Versicolor
            np.random.normal(2.0, 0.27, 50),  # Virginica
        ]
    ),
    "species": ["setosa"] * 50 + ["versicolor"] * 50 + ["virginica"] * 50,
}
df = pd.DataFrame(data)

# Ensure realistic bounds
df["sepal_length"] = df["sepal_length"].clip(4.3, 7.9)
df["sepal_width"] = df["sepal_width"].clip(2.0, 4.4)
df["petal_length"] = df["petal_length"].clip(1.0, 6.9)
df["petal_width"] = df["petal_width"].clip(0.1, 2.5)

# Define numeric columns and normalize to [0, 1] for fair comparison
numeric_cols = ["sepal_length", "sepal_width", "petal_length", "petal_width"]
df_norm = df.copy()
for col in numeric_cols:
    min_val = df[col].min()
    max_val = df[col].max()
    df_norm[col] = (df[col] - min_val) / (max_val - min_val)

# Plot — see default-style-guide.md "Visual Sizing Defaults" for canvas + sizing values
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

colors = {"setosa": IMPRINT_PALETTE[0], "versicolor": IMPRINT_PALETTE[1], "virginica": IMPRINT_PALETTE[2]}

# Vertical reference line at each dimension's axis position - anchors the
# "each variable is a vertical axis" metaphor that parallel coordinates rely on
x = np.arange(len(numeric_cols))
for xi in x:
    ax.axvline(xi, color=INK_SOFT, alpha=0.3, linewidth=0.8, zorder=0)

# Plot parallel coordinates - vectorized via LineCollection instead of a per-row loop
segments = np.stack([np.tile(x, (len(df_norm), 1)), df_norm[numeric_cols].to_numpy()], axis=2)
line_colors = df_norm["species"].map(colors).to_numpy()
lc = LineCollection(segments, colors=line_colors, alpha=0.4, linewidths=2, zorder=2)
ax.add_collection(lc)
ax.set_xlim(x.min() - 0.15, x.max() + 0.15)

# Axis labels with original scale ranges
ax.set_xticks(x)
labels = [
    f"Sepal Length\n({df['sepal_length'].min():.1f}-{df['sepal_length'].max():.1f} cm)",
    f"Sepal Width\n({df['sepal_width'].min():.1f}-{df['sepal_width'].max():.1f} cm)",
    f"Petal Length\n({df['petal_length'].min():.1f}-{df['petal_length'].max():.1f} cm)",
    f"Petal Width\n({df['petal_width'].min():.1f}-{df['petal_width'].max():.1f} cm)",
]
ax.set_xticklabels(labels, fontsize=8, color=INK_SOFT)
ax.set_ylabel("Normalized Value", fontsize=10, color=INK)
ax.set_title("parallel-basic · python · matplotlib · anyplot.ai", fontsize=12, fontweight="medium", color=INK)
ax.tick_params(axis="y", labelsize=8, colors=INK_SOFT)

# Add legend for species
legend_handles = [
    plt.Line2D([0], [0], color=colors["setosa"], linewidth=3, label="Setosa"),
    plt.Line2D([0], [0], color=colors["versicolor"], linewidth=3, label="Versicolor"),
    plt.Line2D([0], [0], color=colors["virginica"], linewidth=3, label="Virginica"),
]
leg = ax.legend(handles=legend_handles, fontsize=8, loc="upper right")
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
plt.setp(leg.get_texts(), color=INK_SOFT)

# Styling
ax.set_ylim(-0.05, 1.05)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for spine in ("left", "bottom"):
    ax.spines[spine].set_color(INK_SOFT)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)  # no bbox_inches='tight'

Part of Basic Parallel Coordinates Plot on anyplot.ai.

Basic Parallel Coordinates Plot — Matplotlib

Python source (Matplotlib)

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